In silico identification of vaccine targets for 2019-nCoV
Identifieur interne : 001006 ( Pmc/Corpus ); précédent : 001005; suivant : 001007In silico identification of vaccine targets for 2019-nCoV
Auteurs : Chloe Hyun-Jung Lee ; Hashem KoohySource :
- F1000Research [ 2046-1402 ] ; 2020.
Abstract
Url:
DOI: 10.12688/f1000research.22507.1
PubMed: 32269766
PubMed Central: 7111504
Links to Exploration step
PMC:7111504Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en"><italic>In silico</italic> identification of vaccine targets for 2019-nCoV</title><author><name sortKey="Hyun Jung Lee, Chloe" sort="Hyun Jung Lee, Chloe" uniqKey="Hyun Jung Lee C" first="Chloe" last="Hyun-Jung Lee">Chloe Hyun-Jung Lee</name><affiliation><nlm:aff id="a1">MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</nlm:aff></affiliation></author><author><name sortKey="Koohy, Hashem" sort="Koohy, Hashem" uniqKey="Koohy H" first="Hashem" last="Koohy">Hashem Koohy</name><affiliation><nlm:aff id="a1">MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32269766</idno><idno type="pmc">7111504</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7111504</idno><idno type="RBID">PMC:7111504</idno><idno type="doi">10.12688/f1000research.22507.1</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">001006</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001006</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main"><italic>In silico</italic> identification of vaccine targets for 2019-nCoV</title><author><name sortKey="Hyun Jung Lee, Chloe" sort="Hyun Jung Lee, Chloe" uniqKey="Hyun Jung Lee C" first="Chloe" last="Hyun-Jung Lee">Chloe Hyun-Jung Lee</name><affiliation><nlm:aff id="a1">MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</nlm:aff></affiliation></author><author><name sortKey="Koohy, Hashem" sort="Koohy, Hashem" uniqKey="Koohy H" first="Hashem" last="Koohy">Hashem Koohy</name><affiliation><nlm:aff id="a1">MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</nlm:aff></affiliation></author></analytic><series><title level="j">F1000Research</title><idno type="eISSN">2046-1402</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><bold>Background:</bold> The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China.</p><p><bold>Methods:</bold> The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0.</p><p><bold>Results:</bold> We report
<italic>in silico</italic> identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a
<italic>de novo</italic> search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential.</p><p><bold>Conclusions:</bold> Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Kock, Ra" uniqKey="Kock R">RA Kock</name></author><author><name sortKey="Karesh, Wb" uniqKey="Karesh W">WB Karesh</name></author><author><name sortKey="Veas, F" uniqKey="Veas F">F Veas</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vita, R" uniqKey="Vita R">R Vita</name></author><author><name sortKey="Mahajan, S" uniqKey="Mahajan S">S Mahajan</name></author><author><name sortKey="Overton, Ja" uniqKey="Overton J">JA Overton</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lee, Ch" uniqKey="Lee C">CH Lee</name></author><author><name sortKey="Koohy, H" uniqKey="Koohy H">H Koohy</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pogorelyy, Mv" uniqKey="Pogorelyy M">MV Pogorelyy</name></author><author><name sortKey="Fedorova, Ad" uniqKey="Fedorova A">AD Fedorova</name></author><author><name sortKey="Mclaren, Je" uniqKey="Mclaren J">JE McLaren</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jurtz, V" uniqKey="Jurtz V">V Jurtz</name></author><author><name sortKey="Paul, S" uniqKey="Paul S">S Paul</name></author><author><name sortKey="Andreatta, M" uniqKey="Andreatta M">M Andreatta</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Karapetyan, Ar" uniqKey="Karapetyan A">AR Karapetyan</name></author><author><name sortKey="Chaipan, C" uniqKey="Chaipan C">C Chaipan</name></author><author><name sortKey="Winkelbach, K" uniqKey="Winkelbach K">K Winkelbach</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ahmed, Sf" uniqKey="Ahmed S">SF Ahmed</name></author><author><name sortKey="Quadeer, Aa" uniqKey="Quadeer A">AA Quadeer</name></author><author><name sortKey="Mckay, Mr" uniqKey="Mckay M">MR McKay</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lee, Ch" uniqKey="Lee C">CH Lee</name></author><author><name sortKey="Koohy, H" uniqKey="Koohy H">H Koohy</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">F1000Res</journal-id><journal-id journal-id-type="iso-abbrev">F1000Res</journal-id><journal-id journal-id-type="pmc">F1000Research</journal-id><journal-title-group><journal-title>F1000Research</journal-title></journal-title-group><issn pub-type="epub">2046-1402</issn><publisher><publisher-name>F1000 Research Limited</publisher-name><publisher-loc>London, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32269766</article-id><article-id pub-id-type="pmc">7111504</article-id><article-id pub-id-type="doi">10.12688/f1000research.22507.1</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group><subject>Articles</subject></subj-group></article-categories><title-group><article-title><italic>In silico</italic> identification of vaccine targets for 2019-nCoV</article-title><fn-group content-type="pub-status"><fn><p>[version 1; peer review: 2 approved]</p></fn></fn-group></title-group><contrib-group><contrib contrib-type="author"><name><surname>Hyun-Jung Lee</surname><given-names>Chloe</given-names></name><role content-type="http://credit.casrai.org/">Data Curation</role><role content-type="http://credit.casrai.org/">Formal Analysis</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Resources</role><role content-type="http://credit.casrai.org/">Software</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Visualization</role><role content-type="http://credit.casrai.org/">Writing – Original Draft Preparation</role><role content-type="http://credit.casrai.org/">Writing – Review & Editing</role><xref ref-type="aff" rid="a1">1</xref></contrib><contrib contrib-type="author"><name><surname>Koohy</surname><given-names>Hashem</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Data Curation</role><role content-type="http://credit.casrai.org/">Funding Acquisition</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Supervision</role><role content-type="http://credit.casrai.org/">Writing – Original Draft Preparation</role><role content-type="http://credit.casrai.org/">Writing – Review & Editing</role><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3640-7043</contrib-id><xref ref-type="corresp" rid="c1">a</xref><xref ref-type="aff" rid="a1">1</xref></contrib><aff id="a1"><label>1</label>MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</aff></contrib-group><author-notes><corresp id="c1"><label>a</label><email xlink:href="mailto:hashem.koohy@rdm.ox.ac.uk">hashem.koohy@rdm.ox.ac.uk</email></corresp><fn fn-type="COI-statement"><p>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type="epub"><day>25</day><month>2</month><year>2020</year></pub-date><pub-date pub-type="collection"><year>2020</year></pub-date><volume>9</volume><elocation-id>145</elocation-id><history><date date-type="accepted"><day>20</day><month>2</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Hyun-Jung Lee C and Koohy H</copyright-statement><copyright-year>2020</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="f1000research-9-24839.pdf"></self-uri><abstract><p><bold>Background:</bold> The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China.</p><p><bold>Methods:</bold> The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0.</p><p><bold>Results:</bold> We report
<italic>in silico</italic> identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a
<italic>de novo</italic> search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential.</p><p><bold>Conclusions:</bold> Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.</p></abstract><kwd-group kwd-group-type="author"><kwd>Coronavirus</kwd><kwd>adaptive immunity</kwd><kwd>immunogenicity</kwd><kwd>T cell cross-reactivity</kwd><kwd>vaccine development</kwd></kwd-group><funding-group><award-group id="fund-1" xlink:href="http://dx.doi.org/10.13039/501100000265"><funding-source>Medical Research Council</funding-source></award-group><funding-statement>This study was funded by the Medical Research Council Human Immunology Unit. </funding-statement></funding-group></article-meta></front><body><sec sec-type="intro"><title>Introduction</title><p>The emergence and rapid spread of the recent novel coronavirus known as 2019-nCoV has posed a serious global health threat
<sup><xref rid="ref-1" ref-type="bibr">1</xref></sup> and has already caused a huge financial burden
<sup><xref rid="ref-2" ref-type="bibr">2</xref></sup>. It has further challenged the scientific and industrial community for quick control practices, and equally importantly to develop effective vaccines to prevent its recurrence. In facing a rapid epidemical outbreak to a novel and unknown pathogen, a key bottleneck for a proper and deep investigation, which is fundamental for vaccine development, is the limited -- to almost no -- access of the scientific community to samples from infected subjects. As such,
<italic>in silico</italic> predictions of targets for vaccines are of high importance and can serve as a guidance to medical and experimental experts for the best and timely use of the limited resources.</p><p>In this regard, we report our recent effort to computationally identify immunogenic and/or cross-reactive peptides from 2019-nCoV. We provide a detailed screen of candidate peptides based on comparison with immunogenic peptides deposited in the Immune Epitope Database and Analysis Resource (IEDB) database including those derived from Severe acute respiratory syndrome-related coronavirus (SARS CoV) along with
<italic>de novo</italic> prediction from 2019-nCoV 9-mer peptides. Here, we found i) 28 SARS-derived peptides having exact matches in 2019-nCoV proteome previously characterized to be immunogenic by
<italic>in vitro</italic> T cell assays, ii) 22 nCoV peptides having a high sequence similarity with immunogenic peptides but with a greater predicted immunogenicity score, and iii) 44 + 19 nCoV peptides predicted to be immunogenic by the iPred algorithm and 1G4 TCR positional weight matrices respectively.</p></sec><sec sec-type="results"><title>Results</title><sec><title>Identification of 28 exact matches to SARS CoV immunogenic peptides by screening all epitopes deposited in IEDB</title><p>We collected all peptides in IEDB (
<xref rid="ref-3" ref-type="bibr">3</xref>, as of 13-02-2020) reported positive in T cell assays and have human as the host organism. We then conducted a local sequence alignment of 10 2019-nCoV open reading frames (ORFs) against 35,225 IEDB peptides, and found 28 exact matches. Surprisingly, all identical hits (towards target peptide length > 3) were from SARS-CoV (
<xref rid="T1" ref-type="table">Table 1</xref>, Data Table 1
<sup><xref rid="ref-4" ref-type="bibr">4</xref></sup>). These peptides have been shown to bind various HLA alleles, although with higher tendency towards HLA-A:02:01, from both class I and class II, and can be target for CD8+ and CD4+ T cells respectively.</p><table-wrap id="T1" orientation="portrait" position="anchor"><label>Table 1. </label><caption><title>28 2019-nCoV peptides having exact matches with immunogenic SARS-CoV peptides.</title></caption><table frame="hsides" rules="groups" content-type="article-table"><thead><tr><th align="center" valign="top" rowspan="1" colspan="1">IEDB.peptide</th><th align="center" valign="top" rowspan="1" colspan="1">2019-nCoV.pattern</th><th align="center" valign="top" rowspan="1" colspan="1">Antigen.Name</th><th align="center" valign="top" rowspan="1" colspan="1">Allele.Name</th></tr></thead><tbody><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TLACFVLAAV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TLACFVLAAV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Membrane glycoprotein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AFFGMSRIGMEVTPSGTW</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AFFGMSRIGMEVTPSGTW</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">N protein</td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ALNTPKDHI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ALNTPKDHI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Nucleoprotein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AQFAPSASAFFGMSR</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AQFAPSASAFFGMSR</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">nucleocapsid protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA class II</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AQFAPSASAFFGMSRIGM</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AQFAPSASAFFGMSRIGM</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">N protein</td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GMSRIGMEV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GMSRIGMEV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Nucleoprotein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ILLNKHIDA</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ILLNKHIDA</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Nucleoprotein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>IRQGTDYKHWPQIAQFA</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>IRQGTDYKHWPQIAQFA</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">N protein</td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>KHWPQIAQFAPSASAFF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>KHWPQIAQFAPSASAFF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">N protein</td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LALLLLDRL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LALLLLDRL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Nucleoprotein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LLLDRLNQL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LLLDRLNQL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Nucleoprotein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LLNKHIDAYKTFPPTEPK</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LLNKHIDAYKTFPPTEPK</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">N protein</td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LQLPQGTTL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LQLPQGTTL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Nucleoprotein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RRPQGLPNNTASWFT</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RRPQGLPNNTASWFT</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">nucleocapsid protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA class I</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>YKTFPPTEPKKDKKKK</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>YKTFPPTEPKKDKKKK</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">N protein</td><td align="center" valign="top" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ILLNKHID</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ILLNKHID</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Nucleoprotein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>MEVTPSGTWL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>MEVTPSGTWL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">nucleocapsid protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-B
<xref ref-type="other" rid="TFN1">*</xref>40:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ALNTLVKQL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ALNTLVKQL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">S protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>FIAGLIAIV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>FIAGLIAIV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Spike glycoprotein precursor</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A2</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LITGRLQSL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LITGRLQSL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Spike glycoprotein precursor</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A2</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>NLNESLIDL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>NLNESLIDL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">S protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>QALNTLVKQLSSNFGAI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>QALNTLVKQLSSNFGAI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">S protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-DRB1
<xref ref-type="other" rid="TFN1">*</xref>04:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RLNEVAKNL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RLNEVAKNL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Spike glycoprotein precursor</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VLNDILSRL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VLNDILSRL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">S protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VVFLHVTYV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VVFLHVTYV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">Spike glycoprotein precursor</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-A
<xref ref-type="other" rid="TFN1">*</xref>02:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GAALQIPFAMQMAYRF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GAALQIPFAMQMAYRF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">S protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-DRA
<xref ref-type="other" rid="TFN1">*</xref>01:01/DRB1
<xref ref-type="other" rid="TFN1">*</xref>07:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>MAYRFNGIGVTQNVLY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>MAYRFNGIGVTQNVLY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">S protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-DRB1
<xref ref-type="other" rid="TFN1">*</xref>04:01</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>QLIRAAEIRASANLAATK</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>QLIRAAEIRASANLAATK</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">S protein</td><td align="center" valign="top" rowspan="1" colspan="1">HLA-DRB1
<xref ref-type="other" rid="TFN1">*</xref>04:01</td></tr></tbody></table><table-wrap-foot><fn><p id="TFN1">*SARS-CoV: Severe acute respiratory syndrome coronavirus</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Identification of 22 2019-nCoV peptides with high degree of similarity to previously reported immunogenic viral peptides</title><p>In addition to 28 identical hits against SARS CoV, we observed a long tail in distribution of normalized alignment scores between 10 2019-nCoV ORFs and 35,225 IEDB peptides (
<xref ref-type="fig" rid="f1">Figure 1A</xref>). We therefore set out to further investigate potential vaccine targets among highly similar sequences.</p><fig fig-type="figure" id="f1" orientation="portrait" position="anchor"><label>Figure 1. </label><caption><title>2019-nCoV peptides with high sequence similarity to immunogenic peptides in IEDB.</title><p><bold>A.</bold> Comparison of normalized sequence alignment score for peptides with exact and non-exact matches.
<bold>B.</bold> Number of target peptides grouped by their source organism.</p></caption><graphic xlink:href="f1000research-9-24839-g0000"></graphic></fig><p>Taking the normalized alignment score of exact matches as a reference, we extracted 2019-nCoV peptides having score greater or equal to 4. As illustrated in
<xref ref-type="fig" rid="f1">Figure 1A</xref>, we observed 45 and 11 peptides having normalized alignment score ≥ 4 and ≥ 5 respectively (
<xref ref-type="fig" rid="f1">Figure 1A</xref> inset). The target peptides were originated from 10 different sources (
<xref ref-type="fig" rid="f1">Figure 1B</xref>) where a total 36 peptides were derived from strains associated to SARS CoV. Of interest, we also observed 7 hits having high sequence similarity to targets from
<italic>Homo sapiens</italic>.</p><p>In order to investigate the extent to which the difference between the source (2019-nCoV) and target (IEDB) peptides influences the immunogenicity of the source peptides we used a recently published immunogenicity model
<sup><xref rid="ref-5" ref-type="bibr">5</xref></sup> to predict and compare the immunogenicity between the source and target peptides (Data Table 2
<sup><xref rid="ref-4" ref-type="bibr">4</xref></sup>).</p><table-wrap id="T2" orientation="portrait" position="anchor"><label>Table 2. </label><caption><title>List of 22 2019-nCoV peptides having a higher predicted immunogenicity score than their target peptides.</title></caption><table frame="hsides" rules="groups" content-type="article-table"><thead><tr><th align="center" valign="top" rowspan="1" colspan="1">IEDB.peptide</th><th align="center" valign="top" rowspan="1" colspan="1">2019-nCoV.pattern</th><th align="center" valign="top" rowspan="1" colspan="1">IEDB.prob</th><th align="center" valign="top" rowspan="1" colspan="1">nCol.prob</th></tr></thead><tbody><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>WYMWLGARY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>WYIWLG</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.999249</td><td align="center" valign="top" rowspan="1" colspan="1">0.999441</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GLMWLSYFV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GLMWLSYFI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.995073</td><td align="center" valign="top" rowspan="1" colspan="1">0.998216</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GLVFLCLQY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GIVFMCVEY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.98123</td><td align="center" valign="top" rowspan="1" colspan="1">0.984127</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TWLTYHGAIKLDDKDPQFKDNVILL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TWLTYTGAIKLDDKDPNFKDQVILL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.925862</td><td align="center" valign="top" rowspan="1" colspan="1">0.975242</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>IGMEVTPSGTWLTYH</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>IGMEVTPSGTWLTY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.903518</td><td align="center" valign="top" rowspan="1" colspan="1">0.919184</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GETALALLLLDRLNQ</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GDAALALLLLDRLNQ</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.853114</td><td align="center" valign="top" rowspan="1" colspan="1">0.900655</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TPSGTWLTYHGAIKL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TPSGTWLTYTGAIKL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.620894</td><td align="center" valign="top" rowspan="1" colspan="1">0.662417</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>SIVAYTMSL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>SIIAYTMSL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.589694</td><td align="center" valign="top" rowspan="1" colspan="1">0.693763</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RRPQGLPNNIASWFT</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RRPQGLPNNTASWFT</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.533253</td><td align="center" valign="top" rowspan="1" colspan="1">0.584355</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>YNLKWN</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>YNL-WN</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.520244</td><td align="center" valign="top" rowspan="1" colspan="1">0.765309</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AGCLIGAEHVDTSYECDI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AGCLIGAEHVNNSYECDI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.503905</td><td align="center" valign="top" rowspan="1" colspan="1">0.56813</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GFMKQYGECLGDINARDL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GFIKQYGDCLGDIAARDL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.471939</td><td align="center" valign="top" rowspan="1" colspan="1">0.506817</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ANKEGIVWVATEGAL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ANKDGIIWVATEGAL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.367723</td><td align="center" valign="top" rowspan="1" colspan="1">0.404796</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>WNPDDY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>WNADLY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.355018</td><td align="center" valign="top" rowspan="1" colspan="1">0.584726</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>PDDYGG</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>PDDFTG</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.334887</td><td align="center" valign="top" rowspan="1" colspan="1">0.527287</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TWLTYHGAIKLDDKDPQF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TWLTYTGAIKLDDKDPNF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.27017</td><td align="center" valign="top" rowspan="1" colspan="1">0.529675</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>DEVNQI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>DEVRQI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.18504</td><td align="center" valign="top" rowspan="1" colspan="1">0.187797</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>SSKRFQPFQQFGRDV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>SNKKFLPFQQFGRDI</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.098384</td><td align="center" valign="top" rowspan="1" colspan="1">0.119472</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>NHDSPDAEL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>NHTSPDVDL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.067808</td><td align="center" valign="top" rowspan="1" colspan="1">0.17889</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TKQYNVTQAF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TKAYNVTQAF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.054818</td><td align="center" valign="top" rowspan="1" colspan="1">0.171488</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VKQMYKTPTLKYFGGFNF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VKQIYKTPPIKDFGGFNF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.018685</td><td align="center" valign="top" rowspan="1" colspan="1">0.135681</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>QKRTATKQYNVTQAF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1"><monospace>QKRTATKAYNVTQAF</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.004891</td><td align="center" valign="top" rowspan="1" colspan="1">0.037776</td></tr></tbody></table></table-wrap><p>We could see a similar (close to identical) immunogenicity scores for a number of IEDB and 2019-nCov peptides especially for those with high immunogenicity scores (
<xref ref-type="fig" rid="f2">Figure 2</xref>). While all 48 can be potential targets, of particular interest were those having higher immunogenicity score than IEDB peptides. Here, we list 22 out of 48 2019-nCoV peptides that scored higher compared to their targets that have been characterized to be immunogenic (
<xref rid="T2" ref-type="table">Table 2</xref>). In this list 15 (68%) 2019-nCov peptides have a score higher than 0.5 whereas only 11(50%) of IEDB get a score immunogenicity score greater than 0.5.</p><fig fig-type="figure" id="f2" orientation="portrait" position="anchor"><label>Figure 2. </label><caption><title>Predicted immunogenicity for IEDB immunogenic vs. 2019-nCoV peptides.</title><p>2019-nCoV peptides having a high sequence similarity to immunogenic peptides and their targets were analysed for their immunogenicity potential by iPred algorithm.</p></caption><graphic xlink:href="f1000research-9-24839-g0001"></graphic></fig><p>It is worth noting that in general predicting immunogenicity of given a peptide is challenging and not a fully solved problem, and therefore current models for predicting immunogenicity are suboptimal. iPred is also not an exception. In fact, we could see that a substantial number of IEDB immunogenic peptides were scored < 0.5 (the threshold score used to classify immunogenic vs non-immunogenic). This led us to ask whether we can gather any other evidence of either immunogenicity or cross-reactivity.</p></sec><sec><title><italic>De novo</italic> search of immunogenic peptides in 2019-nCoV proteome</title><p>As a complementary reciprocal approach, we conducted a
<italic>de novo</italic> search of immunogenic peptides against the 2019-nCov proteome sequence. We scanned 9-mers from 2019-nCoV proteome with a window of 9 amino acids and step length of 1 amino acid (9613 in total). The immunogenicity of 9-mer peptides were predicted using iPred and MHC presentation scores were gauged using NetMHCpan 4.0
<sup><xref rid="ref-6" ref-type="bibr">6</xref></sup> for various HLA types. In this task, we focused on haplotypes common in Chinese and European populations, which include HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles (Data Table 3
<sup><xref rid="ref-4" ref-type="bibr">4</xref></sup>).</p><table-wrap id="T3" orientation="portrait" position="anchor"><label>Table 3. </label><caption><title>2019-nCoV 9-mer peptides predicted to bind 4 different HLA alleles by NetMHCpan 4.0, and those predicted to bind ≥ 3 HLA alleles and immunogenicity score ≥ 0.9 by iPred.</title><p>For different alleles, 0 denotes non-binding and 1 denotes binding predicted for specific HLA allele.</p></caption><table frame="hsides" rules="groups" content-type="article-table"><thead><tr><th align="center" valign="top" rowspan="1" colspan="1">Antigen.epitope</th><th align="center" valign="top" rowspan="1" colspan="1">Imm.prob</th><th align="center" valign="top" rowspan="1" colspan="1">A0101.NB</th><th align="center" valign="top" rowspan="1" colspan="1">A0201.NB</th><th align="center" valign="top" rowspan="1" colspan="1">B0702.NB</th><th align="center" valign="top" rowspan="1" colspan="1">B4001.NB</th><th align="center" valign="top" rowspan="1" colspan="1">C0702.NB</th><th align="center" valign="top" rowspan="1" colspan="1">Total binding HLA</th></tr></thead><tbody><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VQMAPISAM</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.893938</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">4</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>AMYTPHTVL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.862427</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">4</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TLDSKTQSL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.254998</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">4</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>KVDGVVQQL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.191786</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">4</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>KVDGVDVEL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.18632</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">4</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>MADQAMTQM</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.991227</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LEAPFLYLY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.983072</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RTAPHGHVM</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.972153</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>IPFAMQMAY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.961569</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>FLTENLLLY</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.951715</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>YLQPRTFLL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.947743</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>MMISAGFSL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.941318</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ATLPKGIMM</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.926603</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">0</td><td align="center" valign="top" rowspan="1" colspan="1">1</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr></tbody></table></table-wrap><p>Based on MHC presentation and immunogenicity prediction, we detected 5 peptides predicted to bind 4 different HLA alleles of which 2 had strong immunogenicity scores (
<xref ref-type="fig" rid="f3">Figure 3</xref>). For those 65 strong binders to 3 different HLA types, 39 had immunogenicity scores ≥ 0.5 (
<xref rid="T3" ref-type="table">Table 3</xref>). Collectively this analysis suggests a number of 9-mer immunogenic candidates for further experimental validation.</p><fig fig-type="figure" id="f3" orientation="portrait" position="anchor"><label>Figure 3. </label><caption><title><italic>De novo</italic> search of 9-mer 2019-nCoV peptides with MHC presentation and immunogenicity potential.</title><p>The MHC binding was predicted for HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles by NetMHCpan 4.0 and immunogenicity was predicted by iPred.</p></caption><graphic xlink:href="f1000research-9-24839-g0002"></graphic></fig></sec><sec><title>Immunogenicity of 2019-nCoV peptides to 1G4 CD8+ TCR molecule</title><p>While our
<italic>de novo</italic> candidates are appealing shortlisted targets for experimental validation, it does not provide information about target T cell receptors (TCRs). We therefore set out to interrogate the possibility of cross reactivity with one well-studied TCR.</p><p>T cell cross-reactivity has been instrumental for the T cell immunity against both tumor antigens and external pathogens. In that regard, a number of T cells have been extensively characterized including 1G4 CD8+ TCR, which is known to recognize the ‘SLLMWITQC’ peptide presented by HLA-A*02:01. We therefore set out to leverage the data from a recently published study
<sup><xref rid="ref-7" ref-type="bibr">7</xref></sup> and exploit the possibility of cross reactivity of this TCR to any 2019-nCoV peptide.</p><p>Here, we scanned all 9-mers from the 2019-nCoV proteome (9613 peptides) with Binding, Activating and Killing Position Weight Matrices (PWM, see the method section) and associated each peptide with the geometric mean of these three assays as a measure of immunogenicity (Data Table 4
<sup><xref rid="ref-4" ref-type="bibr">4</xref></sup>). The distributions of binding, activation and killing scores along with their multiplicative score and geometric mean are illustrated in
<xref ref-type="fig" rid="f4">Figure 4</xref>. Based on geometric mean, we observed 20 2019-nCoV peptides with a score > 0.8 and 516 peptides > 0.7. The 9-mer peptides with geometric mean > 0.7 and positive HLA-A*02:01 binding prediction by NetMHCpan 4.0 are listed in
<xref rid="T4" ref-type="table">Table 4</xref>.</p><fig fig-type="figure" id="f4" orientation="portrait" position="anchor"><label>Figure 4. </label><caption><title>Distribution of 1G4 TCR positional weight matrix scores for 2019-nCoV peptides.</title><p>The positional weight matrices were obtained from
<xref rid="ref-7" ref-type="bibr">7</xref> and 9613 9-mers generated from 10 2019-nCoV ORFs were computed for their TCR recognition potential.</p></caption><graphic xlink:href="f1000research-9-24839-g0003"></graphic></fig><table-wrap id="T4" orientation="portrait" position="anchor"><label>Table 4. </label><caption><title>2019-nCoV 9-mer peptides with geometric mean ≥ 0.7 by 1G4 TCR positional weight matrix and predicted positive to bind HLA-A*02:01 by NetMHCpan 4.0 (Rank = NetMHCpan rank)</title></caption><table frame="hsides" rules="groups" content-type="article-table"><thead><tr><th align="center" valign="top" rowspan="1" colspan="1">Peptide</th><th align="center" valign="top" rowspan="1" colspan="1">Binding score</th><th align="center" valign="top" rowspan="1" colspan="1">Activation score</th><th align="center" valign="top" rowspan="1" colspan="1">Killing score</th><th align="center" valign="top" rowspan="1" colspan="1">geoMean</th><th align="center" valign="top" rowspan="1" colspan="1">Rank</th><th align="center" valign="top" rowspan="1" colspan="1">Binder</th></tr></thead><tbody><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RIMTWLDMV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.866377428</td><td align="center" valign="top" rowspan="1" colspan="1">0.853995</td><td align="center" valign="top" rowspan="1" colspan="1">0.776303</td><td align="center" valign="top" rowspan="1" colspan="1">0.831249</td><td align="center" valign="top" rowspan="1" colspan="1">0.3481</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ALNTLVKQL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.802453741</td><td align="center" valign="top" rowspan="1" colspan="1">0.75073</td><td align="center" valign="top" rowspan="1" colspan="1">0.785957</td><td align="center" valign="top" rowspan="1" colspan="1">0.779413</td><td align="center" valign="top" rowspan="1" colspan="1">0.6159</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LLLDRLNQL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.809895414</td><td align="center" valign="top" rowspan="1" colspan="1">0.7752</td><td align="center" valign="top" rowspan="1" colspan="1">0.741096</td><td align="center" valign="top" rowspan="1" colspan="1">0.774888</td><td align="center" valign="top" rowspan="1" colspan="1">0.0423</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>MIAQYTSAL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.766262499</td><td align="center" valign="top" rowspan="1" colspan="1">0.789511</td><td align="center" valign="top" rowspan="1" colspan="1">0.749477</td><td align="center" valign="top" rowspan="1" colspan="1">0.768242</td><td align="center" valign="top" rowspan="1" colspan="1">0.9238</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VLSTFISAA</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.799672451</td><td align="center" valign="top" rowspan="1" colspan="1">0.756117</td><td align="center" valign="top" rowspan="1" colspan="1">0.687278</td><td align="center" valign="top" rowspan="1" colspan="1">0.746239</td><td align="center" valign="top" rowspan="1" colspan="1">0.536</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>NVLAWLYAA</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.761297552</td><td align="center" valign="top" rowspan="1" colspan="1">0.686117</td><td align="center" valign="top" rowspan="1" colspan="1">0.739944</td><td align="center" valign="top" rowspan="1" colspan="1">0.728423</td><td align="center" valign="top" rowspan="1" colspan="1">1.4457</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>RLANECAQV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.783161706</td><td align="center" valign="top" rowspan="1" colspan="1">0.719705</td><td align="center" valign="top" rowspan="1" colspan="1">0.680504</td><td align="center" valign="top" rowspan="1" colspan="1">0.726572</td><td align="center" valign="top" rowspan="1" colspan="1">0.2049</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>KLLKSIAAT</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.748896679</td><td align="center" valign="top" rowspan="1" colspan="1">0.708996</td><td align="center" valign="top" rowspan="1" colspan="1">0.697463</td><td align="center" valign="top" rowspan="1" colspan="1">0.718118</td><td align="center" valign="top" rowspan="1" colspan="1">1.0923</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>QLSLPVLQV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.70128376</td><td align="center" valign="top" rowspan="1" colspan="1">0.715259</td><td align="center" valign="top" rowspan="1" colspan="1">0.708405</td><td align="center" valign="top" rowspan="1" colspan="1">0.708293</td><td align="center" valign="top" rowspan="1" colspan="1">0.4768</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>VQMAPISAM</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.729320768</td><td align="center" valign="top" rowspan="1" colspan="1">0.698514</td><td align="center" valign="top" rowspan="1" colspan="1">0.689612</td><td align="center" valign="top" rowspan="1" colspan="1">0.705612</td><td align="center" valign="top" rowspan="1" colspan="1">1.4677</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LLLTILTSL</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.7131709</td><td align="center" valign="top" rowspan="1" colspan="1">0.715194</td><td align="center" valign="top" rowspan="1" colspan="1">0.680064</td><td align="center" valign="top" rowspan="1" colspan="1">0.702623</td><td align="center" valign="top" rowspan="1" colspan="1">0.2712</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>SVLLFLAFV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.736972762</td><td align="center" valign="top" rowspan="1" colspan="1">0.690855</td><td align="center" valign="top" rowspan="1" colspan="1">0.679534</td><td align="center" valign="top" rowspan="1" colspan="1">0.70202</td><td align="center" valign="top" rowspan="1" colspan="1">1.1449</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>LMWLIINLV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.727847374</td><td align="center" valign="top" rowspan="1" colspan="1">0.681119</td><td align="center" valign="top" rowspan="1" colspan="1">0.694007</td><td align="center" valign="top" rowspan="1" colspan="1">0.700717</td><td align="center" valign="top" rowspan="1" colspan="1">1.304</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr></tbody></table></table-wrap><p>We further analysed the MHC binding propensities and gathered peptides not only predicted positive by NetMHCpan but also to have leucine (L) and valine (V) in anchor positions 2 and 9 respectively. This led to identification of 44 2019-nCoV peptides of which 2 peptides had immunogenicity score > 0.7 and 12 peptides > 0.6 (
<xref rid="T5" ref-type="table">Table 5</xref>). Thus, here we provide the list of peptides that are potential targets for 1G4 TCR recognition for subjects with HLA-A02:01 haplotype.</p><table-wrap id="T5" orientation="portrait" position="anchor"><label>Table 5. </label><caption><title>2019-nCoV 9-mer peptides having leucine-valine in anchor positions.</title><p>Peptides have geometric mean ≥ 0.6 and ≤ 0.7 (for those ≥ 0.7, refer to
<xref rid="T4" ref-type="table">Table 4</xref>) by 1G4 TCR positional weight matrix and predicted positive for HLA-A*02:01 binding by NetMHCpan 4.0 (Rank = NetMHCpan rank).</p></caption><table frame="hsides" rules="groups" content-type="article-table"><thead><tr><th align="center" valign="top" rowspan="1" colspan="1">Peptide</th><th align="center" valign="top" rowspan="1" colspan="1">Binding score</th><th align="center" valign="top" rowspan="1" colspan="1">Activation score</th><th align="center" valign="top" rowspan="1" colspan="1">Killing score</th><th align="center" valign="top" rowspan="1" colspan="1">geoMean</th><th align="center" valign="top" rowspan="1" colspan="1">Rank</th><th align="center" valign="top" rowspan="1" colspan="1">Binder</th></tr></thead><tbody><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>TLMNVLTLV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.723687</td><td align="center" valign="top" rowspan="1" colspan="1">0.658986</td><td align="center" valign="top" rowspan="1" colspan="1">0.652178</td><td align="center" valign="top" rowspan="1" colspan="1">0.677534</td><td align="center" valign="top" rowspan="1" colspan="1">0.0444</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>QLEMELTPV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.711291</td><td align="center" valign="top" rowspan="1" colspan="1">0.651003</td><td align="center" valign="top" rowspan="1" colspan="1">0.608605</td><td align="center" valign="top" rowspan="1" colspan="1">0.655625</td><td align="center" valign="top" rowspan="1" colspan="1">1.6769</td><td align="center" valign="top" rowspan="1" colspan="1">WB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>MLAKALRKV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.668756</td><td align="center" valign="top" rowspan="1" colspan="1">0.610664</td><td align="center" valign="top" rowspan="1" colspan="1">0.65968</td><td align="center" valign="top" rowspan="1" colspan="1">0.645854</td><td align="center" valign="top" rowspan="1" colspan="1">0.3524</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>GLFKDCSKV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.675952</td><td align="center" valign="top" rowspan="1" colspan="1">0.632375</td><td align="center" valign="top" rowspan="1" colspan="1">0.594753</td><td align="center" valign="top" rowspan="1" colspan="1">0.633494</td><td align="center" valign="top" rowspan="1" colspan="1">0.2677</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>ALSKGVHFV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.652549</td><td align="center" valign="top" rowspan="1" colspan="1">0.604952</td><td align="center" valign="top" rowspan="1" colspan="1">0.586236</td><td align="center" valign="top" rowspan="1" colspan="1">0.613954</td><td align="center" valign="top" rowspan="1" colspan="1">0.0425</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><monospace>YLNTLTLAV</monospace></td><td align="center" valign="top" rowspan="1" colspan="1">0.624147</td><td align="center" valign="top" rowspan="1" colspan="1">0.610826</td><td align="center" valign="top" rowspan="1" colspan="1">0.575445</td><td align="center" valign="top" rowspan="1" colspan="1">0.603119</td><td align="center" valign="top" rowspan="1" colspan="1">0.0453</td><td align="center" valign="top" rowspan="1" colspan="1">SB</td></tr></tbody></table></table-wrap></sec></sec><sec sec-type="discussion"><title>Discussion</title><p>In this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments. We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV. However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency. Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals.</p><p>With a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings. At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides
<sup><xref rid="ref-8" ref-type="bibr">8</xref></sup>. Our
<italic>in silico</italic> approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including
<italic>de novo</italic> and cross reactive candidates. On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results. Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak.</p><p>Our study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides.</p><p>In this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices. Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation.</p></sec><sec sec-type="methods"><title>Method</title><sec><title>Data acquisition</title><p>2019-nCoV open reading frame sequences were downloaded from NCBI (
<ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/nuccore/MN908947">MN908947.3</ext-link>). All sequences subjected for analysis are deposited in GitHub repository.</p></sec><sec><title>Data analysis</title><p>All subsequent analyses have been conducted in
<ext-link ext-link-type="uri" xlink:href="https://r-project.org/">R</ext-link> 3.6.1.</p></sec><sec><title>Sequence similarity comparison</title><p>The sequence similarity between 2019-nCoV open reading frames and previously characterized immunogenic peptides in IEDB was analysed by local alignment using R ‘pairwiseAlignment’ function from
<ext-link ext-link-type="uri" xlink:href="http://bioconductor.org/packages/3.10/bioc/html/Biostrings.html">Biostrings</ext-link> v2.40.2 package. The local alignment utilized BLOSUM62 matrix, gapOpening of 5 and gapExtension of 5. The alignment score was normalized by length of target peptides.</p></sec><sec><title>Immunogenicity prediction</title><p>We have used
<ext-link ext-link-type="uri" xlink:href="https://github.com/antigenomics/ipred">iPred</ext-link><sup><xref rid="ref-5" ref-type="bibr">5</xref></sup> to predict immunogenicity of each given peptide. Briefly, iPred employs peptides’ length and physicochemical properties of amino acids modelled by sums of ten Kidera factors and associates a score to each peptide reflecting its likelihood of recognition by a T cell.</p></sec><sec><title>Predicting presentation by MHCs</title><p>In order to predict peptide binding to MHC we used
<ext-link ext-link-type="uri" xlink:href="http://www.cbs.dtu.dk/services/NetMHCpan/">NetMHCpan</ext-link> V4
<sup><xref rid="ref-6" ref-type="bibr">6</xref></sup>. This version of NetMHCpan that comes with a number of improvements, incorporate both eluted ligand and peptide binding affinity data into a neural network model to predict MHC presentation of each given peptide.</p></sec><sec><title>Predicting cross reactivity to 1G4 TCR</title><p>To gauge the level of 1G4 TCR cross-reactivity to list of 2019-nCoV virus, we have leveraged the data from a recently published study
<sup><xref rid="ref-7" ref-type="bibr">7</xref></sup>. 1G4 or NY-ESO-1-specific TCR is a very well-studied and clinically efficacious TCR which recognize the peptide ‘SLLMWITQC’ presented by HLA-A*02:01. Karapetyan
<italic>et al.</italic> have recently provided data from three experimental assays reflecting Binding, Activating and Killing upon each mutation at each position of all possible 9-mers using these three datasets. In a similar way to the original paper, we trained three Position Weight Matrices named B, A and K respectively from Binding, Activating and Killing assay. We defined the cross-reactivity score of a given 9-mer sequence as the geometric mean of B, A and K.</p><p>We then scanned 2019-nCoV virus protein sequence with each of B, A and K PWMs and associated each of 9613 9-mers with a cross reactivity score. At the same we utilized NetMHCpan and associated each 9-mer with its presentation score. Our final list of cross-reactive candidate peptides were those with a cross-reactivity sore >= 0.8 and reported as strong binders from NetMHCpan and have ‘L’ and ‘V’ amino acids at anchor positions. The custom R codes are accessible from GitHub repository (see software availability
<sup><xref rid="ref-4" ref-type="bibr">4</xref></sup>).</p></sec></sec><sec><title>Software availability</title><p>Replication code:
<ext-link ext-link-type="uri" xlink:href="https://github.com/ChloeHJ/Vaccine-target-for-2019-nCoV.git">https://github.com/ChloeHJ/Vaccine-target-for-2019-nCoV.git</ext-link></p><p>Archived source code at time of publication:
<ext-link ext-link-type="uri" xlink:href="http://doi.org/10.5281/zenodo.3676908">http://doi.org/10.5281/zenodo.3676908</ext-link><sup><xref rid="ref-4" ref-type="bibr">4</xref></sup></p><p>License:
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</ext-link></p></sec><sec sec-type="data-availability"><title>Data availability</title><sec><title>Source data</title><p>2019-nCoV open reading frame sequences were downloaded from NCBI (
<ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/nuccore/MN908947">MN908947.3</ext-link>).</p></sec><sec><title>Underlying data</title><p>Zenodo:
<italic>In silico</italic> identification of vaccine targets for 2019-nCoV (Data tables).
<ext-link ext-link-type="uri" xlink:href="http://doi.org/10.5281/zenodo.3676886">http://doi.org/10.5281/zenodo.3676886</ext-link><sup><xref rid="ref-9" ref-type="bibr">9</xref></sup></p><p>This project contains the following underlying data:
<list list-type="simple"><list-item><label>– </label><p>Table1 nCoV peptides having exact match with immunogenic SARS CoV peptides.xlsx (Table of nCoV peptides having exact match with immunogenic SARS CoV peptides)</p></list-item><list-item><label>– </label><p>Table2 nCoV peptides with high sequence similarity with immunogenic IEDB peptides.csv (Table of peptides with high sequence similarity with immunogenic IEDB peptides)</p></list-item><list-item><label>– </label><p>Table3
<italic>de novo</italic> search on 9-mer nCoV for immunogenic peptides by NetMHCpan and iPred.csv (Table of results of
<italic>de novo</italic> search on 9-mer nCoV for immunogenic peptides by NetMHCpan and iPred)</p></list-item><list-item><label>– </label><p>Table4
<italic>de novo</italic> search on 9-mer nCoV for immunogenic peptides by NetMHCpan and PWM.xlsx (Table of results of
<italic>de novo</italic> search on 9-mer nCoV for immunogenic peptides by NetMHCpan and PWM)</p></list-item></list></p><p>Data are available under the terms of the
<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International license</ext-link> (CC-BY 4.0).</p></sec></sec></body><back><ack><title>Acknowledgements</title><p>We acknowledge further appreciate assistance and computing support from Unit and WIMM Centre for Computational Biology at MRC Weatherall Institute of Molecular Medicine. We thank G. Napolitani and M. Salio for insightful discussions about the project.</p></ack><ref-list><ref id="ref-1"><label>1</label><mixed-citation publication-type="journal"><collab>World Health Organization</collab>:
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<source><italic toggle="yes">Zenodo.</italic></source><year>2020</year><pub-id pub-id-type="doi">10.5281/zenodo.3676886</pub-id></mixed-citation></ref></ref-list></back><sub-article id="report60502" article-type="peer-review"><front-stub><article-id pub-id-type="doi">10.5256/f1000research.24839.r60502</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>De Palma</surname><given-names>Raffaele</given-names></name><xref ref-type="aff" rid="r60502a1">1</xref><xref ref-type="aff" rid="r60502a2">2</xref><role>Referee</role></contrib><aff id="r60502a1"><label>1</label>DIMI, Department of Internal Medicine, University of Genova, Genova, Italy</aff><aff id="r60502a2"><label>2</label>IBBC (Istituto di Biochimica e Biologia Cellulare), CNR-Napoli, Naples, Italy</aff></contrib-group><author-notes><fn fn-type="COI-statement"><p><bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type="epub"><day>31</day><month>3</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 De Palma R</copyright-statement><copyright-year>2020</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type="peer-reviewed-article" id="d35e2494" ext-link-type="doi" xlink:href="10.12688/f1000research.22507.1">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>The Authors use an
<italic>in silico</italic> approach to identify antigenic peptides derived from 2019-nCoV. First, they screened ten open reading frame of 2019-nCoV sequence used the IEDB database, finding a series of peptides potentially immunogenic. The first piece of data relies on the identification of 28 peptides that were exactly matching SARS CoV peptides. Moreover, using combinatory approaches, modelling HLA and TCR binding, they identified 13 peptides potentially able to bind a given TCR in HLA A2 restriction fashion, and cutting a list of peptides able to bind several HLA alleles and characterizing several peptides that may be de novo candidates or crossreactive peptides to be used either to study immune response to 2019-nCoV or to set a vaccine.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article><sub-article id="report60504" article-type="peer-review"><front-stub><article-id pub-id-type="doi">10.5256/f1000research.24839.r60504</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Wilkinson</surname><given-names>Katalin</given-names></name><xref ref-type="aff" rid="r60504a1">1</xref><xref ref-type="aff" rid="r60504a2">2</xref><role>Referee</role><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-9796-2040</contrib-id></contrib><aff id="r60504a1"><label>1</label>Tuberculosis Laboratory, The Francis Crick Institute, London, UK</aff><aff id="r60504a2"><label>2</label>Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa</aff></contrib-group><author-notes><fn fn-type="COI-statement"><p><bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type="epub"><day>5</day><month>3</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Wilkinson K</copyright-statement><copyright-year>2020</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type="peer-reviewed-article" id="d35e2563" ext-link-type="doi" xlink:href="10.12688/f1000research.22507.1">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This is an important manuscript identifying potential vaccine targets for 2019-nCoV, using computational prediction. In the absence of patient samples, such approaches are instrumental to guide towards quick and efficient identification of vaccine candidates. The authors used 10 open reading frame sequences of 2019-nCoV deposited at NCBI and conducted sequence alignment against immunogenic peptides deposited in the Immune Epitope Database and Analysis Resource (IEDB) database. They identified 28 peptides with sequences matching exactly to severe acute respiratory syndrome-related coronavirus (SARS CoV), that have previously been characterised as immunogenic by T cell assays. These findings are very promising and have the added benefit of potentially developing a vaccine against both SARS and COVID-19.</p><p> Additional peptides were identified to most likely bind common Chinese and European HLA alleles and have high immunogenicity potential. The authors provide a shortlist of peptides as potential vaccine candidates. While this manuscript presents a good model for identifying such targets, a comment should be included in the discussion about the necessity of expanding the analysis to include wider HLA allele types, considering that the virus is likely to spread worldwide.</p><p> Minor comment: Please explain the methods and analysis in greater details, including the following terms:
<list list-type="order"><list-item><p>‘Towards target peptide length > 3’.</p></list-item><list-item><p>‘Normalized alignment scores’ (and their scale, such as the significance of 4 for the data presented in Figure 1).</p></list-item><list-item><p>The importance of leucine (L) and valine (V) in anchor positions for MHC binding (for data presented in Table 5).</p></list-item></list></p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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